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>Low rolling resistance tires number one problem has been bad road comfort.

Which surprises me. I fitted low rolling resistance tyres to my bike and experienced improvements in ride comfort, traction, and significantly reduced rolling resistance (tire wear was increased though as these are technically "racing" tyres). When it is your legs powering the vehicle you can really feel all this. On bikes they achieve the improvements with suppler casing (which increases ride comfort) and softer/suppler rubber (which increases traction and ride comfort but decreases tire life).

(And why is it that people assume that low rolling resistance has anything to do with the coefficient of friction and traction?)

I disagree that economics isn't a science - it is. Whether or not this paper is bad science is beside the point from your rather broad generalisation to the whole of economics. You seem to be mistaking the inherent difficulty of the subject with the quality of the practitioners.

The distinction between "hard" and "soft" is usually the ability to conduct experiments to verify your hypothesis. In "soft" sciences people get really annoyed when you arbitrarily experiment on them. Something about "ethics". But, for some reason, hydrogen atoms never get annoyed when you experiment on them. And that makes for a world of difference in what you can achieve. But that doesn't change the underlying fact that people are forming hypotheses and testing them and applying the scientific method to the whole shebang.

So now, let is talk about your familiarity with economics. You seem to claim to have read and understood a bunch of it with your statement "is typical for most of the junk that economists push". So, how much have you actually read? Or do you just read the Slashdot summary and claim expertise based on that?

Many commenters seem to be mistaking some idealised thing called the Scientific Method with what is actually practiced in the real world when they claim that the scientific method is not being tested. Damn straight this is testing the scientific method - warts and all. If the scientific method as practiced in the real world does not deliver papers that provide sufficient detail to be reproduced then it is not working properly and is broken. In fact, I'm sure that most people who actually publish papers will acknowledge that the peer review process is broken and does not approach this idealised notion of what peer review is in many people's minds. If peer review is broken then a critical element of the scientific method is also broken.

Reproducing results is hard every time I have done it - and that includes times when I have had access to the exact data used by the authors. (And sometimes even the exact code used by the authors - because I was using a later version of the statistical package and results were not consistent between versions.)

So, if people want to claim that the Scientific Method is perfect and this is not a test of it - it would be interesting if they could point to anywhere this idealised Scientific Method is actually practiced (as opposed the the flawed implementation that seems to be practiced in pretty much any field I have ever become acquainted with).

You would include a whole swathe of science in your dismissal of anything that is not experimental. We have, for example, only one Earth and one Universe and people have yet to conduct experiments in star formation.

But, there are also things called natural experiments where there is natural randomisation. An example are studies of twins who were separated by adoption. In this case, you know that the genetics are the same and only the environment differs. One can make valid inferences from these natural experiments.

I will try to inform you a little about economics (speaking as the holder of both a BSc and PhD in Economics):

The key difference is that economics and social sciences are mostly non-experimental (people don't take kindly to you arbitrarily changing their parents, education, or wealth - which is the 'experimental' way of establishing cause and effect). This means that the statistical issues are orders of magnitude larger than those that exist in experimental sciences. In an experimental science you can go off and get new data where you have controlled for most everything except the effect you are interested in and a simple regression will generally be all you need. In a non-experimental science you are stuck with the data that nature has given you. As a result you need to be very careful to get meaningful results. But, in case you are doubting, you can get meaningful results if you are careful enough.

Thus, my second point: Economics is not soft headed. In fact, it is very hard headed because you need to be when you are dealing with data that are generally speaking - crap. There are so many ways you can be mislead by non-experimental data and you need to be very hard-headed to avoid this. I won't claim mistakes haven't been made, but those mistakes are the reason economics has gotten much better at dealing with this than many people might realise. But, there is only so much you can do when the data are the way they are.

So don't assume the difficulty of getting solid results in economics reflects the ability of the practitioners rather than the raw materials you are dealing with.

I ride a flat-bar roadie to commute and the ability to sit on 25mph with the cars is very useful (made a comment like that somewhere up above). Manhole covers and potholes have not caused me any issues and there are enough of them around where I ride.

The gearing might be just enough - but the whole setup is not ideal. On my commute I'll hit 25 mph in a number of places that mean I am keeping up with traffic and, as a result, being much safer. If you are doing 25mph on knobbies you're doing well. And with 26" wheels you'll be spinning that 44/11 out to hit 25mph.

On the other hand, I find a 50T and 700Cx23mm wheels perfect for my commute. The rubbish roads and many manhole covers are not a practical problem.

Because the from address is invariably forged, you do nothing with a bounce. In fact, it's worse than nothing, because you create backscatter. I have suffered from backscatter and it is a pain - it just multiplies the spam problem. So, could I request that you just stop it!

If you actually know the person who is sending you the email then you should try a more personal approach rather than a passive aggressive bounce.

While the spammy advertisement would normally warrant no attention, it does raise a point that is worth noting:

Because the from address is invariably forged, you do nothing with a bounce. In fact, it's worse than nothing, because you create backscatter. I have suffered from backscatter and it is a pain - it just multiplies the spam problem. So, could I request that you just stop it!

Consider also that most researchers run more than 20 regressions when testing their data. That means that the 95% significance level is grossly overstated.

The key is that the 95% level applies only if you don't data snoop beforehand and only if you run the regression once and only once. The true significance levels of many studies that claim a 95% level is likely to be 50% when you consider all the pretesting and data snooping that goes on in reality - not the rather idealised setup that is reported in the journal publication.

When you have a search function that works - really works - organisation isn't so important.

For example, the difference between my use of Mail at home under OS X and Outlook at work is mindboggling. With Mail, I type in a few key words and by the time I've finished typing there is a list of relevant emails, usually containing the one I'm looking for. At work, I have yet to find anything in Outlook using the search function - half the time it doesn't even finish searching before I give up and try something else.

There is a perfectly good name for the thing that some people call "vegetarian chilli con carne" - it is chilli. It reflects the problems that occur when words cross languages and people don't understand what they mean ("vegetarian chilli with meat" - really?). This is not the same as a fake Rolex. But if you think vegetarian chilli con carne is acceptable then so too should be "vegetarian chilli-con-carne with meat" and so on to any order or recursion you can stand.

Rolling resistance is not related to the coefficient of friction of the rubber. It is primarily related to the suppleness of the tyre casing allowing it to roll over minor variations in the road surface without giving up lots of energy in heat associated with the deformation of the tyre. (It is also related to inflation pressure - which is one reason why car manufacturers recommend you maintain your tyres at optimum pressure.)

These sorts of tyres are well know for bicycles and the effects can be dramatic and noticeable when it is your own legs supplying the power rather than an engine. Indeed, the tyres with the lowest rolling resistance also tend to have the stickiest rubber. When I ungraded my bike tyres I got: lower rolling resistance, better cornering, and better wet weather performance. On a bike at least, they also give a more comfortable ride because you are not bouncing over the micro-contours of the road nearly as much.

Consider a simple binary choice question. This is easily modelled by the binomial distribution which has well understood distributions. (Other distrbutions may be relevant but the principles remain pretty constant across them all.) The standard deviation is given by sqrt[np(1-p)] where n is the sample size and p is the probability of the observation you are interested in (the mean is np so in what follows I will be dividing by n to talk about percentages if you are taking notes). For example, are you male? If the true p is, say, 75% then you need a sample size of approximately 833 to get a 95% confidence interval (2 s.d.) of +/- 3%.

You might also note that the closer the true p is to 50%, the larger the sample size needed. If the true p is 50% you need a sample size of approximately 1100 for the same confidence interval. Furthermore, if you want to get it within 1%, the sample size goes up dramatically - to 10,000.

The population size is pretty much irrelevant. The population matters for ensuring that your sampling is truly random, but political pollsters can use the same sample sizes in Australia (pop ~20 million) as in the US (pop ~300 million) for similar accuracy. (Sampling bias is the reason that political polls can be out by so much - if you call households during work hours you are going to get a very different sample of people than if you call at dinner time.)